A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification

• URL: http://arxiv.org/abs/2403.11135v1
• Date: Sun, 17 Mar 2024 08:09:48 GMT
• Title: A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
• Authors: Mahdie Ahmadi, Nader Karimi, Shadrokh Samavi,
• Abstract summary: This paper introduces a novel deep-learning approach for improved breast cancer classification. Our method hinges on the Residual Dual-Shuffle Attention Network (DRDA-Net), inspired by ShuffleNet's efficient architecture. MobileNet ensures fast execution even on devices with limited resources without sacrificing performance.
• Score: 4.371891660358126
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and early detection of breast cancer is essential for successful treatment. This paper introduces a novel deep-learning approach for improved breast cancer classification in histopathological images, a crucial step in diagnosis. Our method hinges on the Dense Residual Dual-Shuffle Attention Network (DRDA-Net), inspired by ShuffleNet's efficient architecture. DRDA-Net achieves exceptional accuracy across various magnification levels on the BreaKHis dataset, a breast cancer histopathology analysis benchmark. However, for real-world deployment, computational efficiency is paramount. We integrate a pre-trained MobileNet model renowned for its lightweight design to address computational. MobileNet ensures fast execution even on devices with limited resources without sacrificing performance. This combined approach offers a promising solution for accurate breast cancer diagnosis, paving the way for faster and more accessible screening procedures.

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